In this study, the problem of shallow parsing of Hindi-English code-mixed social media text (CSMT) has been addressed. We have annotated the data, developed a language identifier, a normalizer, a part-of-speech tagger and a shallow parser. To the best of our knowledge, we are the first to attempt shallow parsing on CSMT. The pipeline developed has been made available to the research community with the goal of enabling better text analysis of Hindi English CSMT. The pipeline is accessible at 1 .
This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.
The paper has examined state-level trends and patterns in crop diversification in India for the period 1990-91 to 2011-12 using Simpson Index of Diversification (SID) and panel regression analysis. The study has revealed that cropping pattern at state level is transforming from foodgrains to high-value crops but the transformation is not uniform across the states/regions. The values of SID have confirmed that the agricultural economy has diversified in all the states with some fluctuations in case of food crops and non-food crops. The results of Fixed Effect Model have revealed cropping intensity, average annual rainfall and gross irrigated area to be the major determinants of crop diversification. The study has suggested that policy support in terms of enhanced cropping intensity, gross irrigated area, insurance coverage, investment in agricultural research and education, and technology development need to be extended to the farmers.
Stuttering is a neuro-development disorder during which normal speech flow is not fluent. Traditionally Speech-Language Pathologists used to assess the extent of stuttering by counting the speech disfluencies manually. Such sorts of stuttering assessments are arbitrary, incoherent, lengthy, and error-prone. The present study focused on objective assessment to speech disfluencies such as prolongation and syllable, word, and phrase repetition. The proposed method is based on the Weighted Mel Frequency Cepstral Coefficient feature extraction algorithm and deep-learning Bidirectional Long-Short term Memory neural network for classification of stuttered events. The work has utilized the UCLASS stuttering dataset for analysis. The speech samples of the database are initially preprocessed, manually segmented, and labeled as a type of disfluency. The labeled speech samples are parameterized to Weighted MFCC feature vectors. Then extracted features are inputted to the Bidirectional-LSTM network for training and testing of the model. The effect of different hyper-parameters on classification results is examined. The test results show that the proposed method reaches the best accuracy of 96.67%, as compared to the LSTM model. The promising recognition accuracy of 97.33%, 98.67%, 97.5%, 97.19%, and 97.67% was achieved for the detection of fluent, prolongation, syllable, word, and phrase repetition, respectively.
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